Accelerated linear algebra compiler for computationally efficient numerical models: Success and potential area of improvement.

ABSTRACT: The recent dramatic progress in machine learning is partially attributed to the availability of high-performant computers and development tools. The accelerated linear algebra (XLA) compiler is one such tool that automatically optimises array operations (mostly fusion to reduce memory operations) and compiles the optimised operations into high-performant programs specific to target computing platforms. Like machine-learning models, numerical models are often expressed in array operations, and thus their performance can be boosted by XLA. This study is the first of its kind to examine the efficiency of XLA for numerical models, and the efficiency is examined stringently by comparing its performance with that of optimal implementations. Two shared-memory computing platforms are examined-the

SUBMITTER: He X 

PROVIDER: S-EPMC9956871 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature